MS: Multiple Segments With Combinatorial Approach for Mining Frequent Itemsets Over Data Streams

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Provided by: NGM College
Topic: Big Data
Format: PDF
Mining frequent itemsets in data stream applications is beneficial for a number of purposes such as knowledge discovery, trend learning, fraud detection, transaction prediction and estimation. In data streams, new data are continuously coming as time advances. It is costly even impossible to store all streaming data received so far due to the memory constraint. It is assumed that the stream can only be scanned once and hence if an item is passed, it cannot be revisited, unless it is stored in main memory.
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